Disclosed are a method and a device for detecting pulmonary nodule in Computed Tomography (CT) image, as well as a computer-readable storage medium. The method for detecting pulmonary nodule in CT image includes: obtaining a CT image to be detected, performing a pixel segmentation processing on the CT image through a pre-stored three-dimensional convolutional neural pixel segmentation network, to obtain a probability graph corresponding to the CT image, and obtaining a candidate nodule region by marking a connected domain on the probability graph; and predicting the candidate nodule region by various pre-stored prediction models corresponding to different three-dimensional convolutional neural network classifiers, to obtain various probability prediction values of the candidate nodule region, and comprehensively processing the various probability prediction values to obtain a classification result of the candidate nodule region.

A method for identification of an optimal image within a sequence of image frames includes inputting the sequence of images into a computer processor configured for executing a plurality of neural networks and applying a sliding window to the image sequence to identify a plurality of image frame windows. The image frame windows are processed using a first neural network trained to classify the image frames according to identified spatial features. The image frame windows are also processed using a second neural network trained to classify the image frames according to identified serial features. The results of each classification are concatenated to separate each of the image frame windows into one of two classes, one class containing the optimal image. An output is generated to display image frame windows classification as including the optimal image.

Medical imaging study summary engine mechanisms are provided. The mechanisms receive a medical imaging study having data representing a plurality of medical images of a patient. The mechanisms generate a temporal trajectory data structure of at least a subset of the medical images in the plurality of medical images, wherein the temporal trajectory data structure specifies topological changes in temporally subsequent medical images in the plurality of medical images. The mechanisms select medical image data corresponding to selected medical images from the medical imaging study data structure based on the temporal trajectory data structure. The mechanisms output the selected medical image data via a medical imaging study user interface.

A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

A medical scan report labeling system is operable to transmit a medical report that includes natural language text to a first client device for display. Identified medical condition term data is received from the first client device in response. An alias mapping pair in a medical label alias database is identified by determining that a medical condition term of the alias mapping pair compares favorably to the identified medical condition term data. A medical code that corresponds to the alias mapping pair and a medical scan that corresponds to the medical report are transmitted to a second client device of an expert user for display, and accuracy data is received from the second client device in response. The medical code is mapped to the first medical scan in the medical scan database when the accuracy data indicates that the medical code compares favorably to the medical scan.

An imaging support apparatus that supports imaging by a radiation imaging apparatus which obtains a plurality of frame images by successively performing a plurality of radiation imaging operations to a subject. The imaging support apparatus includes a hardware processor that determines a number of the frame images to be obtained by the radiation imaging apparatus by analyzing one or more initial frame images, each of the initial frame images being an initial frame image obtained early in the radiation imaging operations by the radiation imaging apparatus.

In one aspect, multiscale filtering is used to normalize the intensities of voxels in an MRI image. A multiscale filter is applied to the raw MRI image. This image is compared to the original image. Luma aberrations (i.e., intensity variations) are corrected based on this comparison. In one approach, the intensity of the image is increased for voxels that are dimmer than in the multiscale filtered version, and decreased for voxels that are brighter than the multiscale filtered version. In another aspect, additional features are created based on multiscale gradients. These may be used in combination with other approaches to segment the MRI image. Voxels with positive gradients may represent brain gray matter bordered by brain white matter. Voxels with negative gradients may represent brain white matter bordered by brain grain matter.

A method of determining the position and orientation of an invasive medical device is described using altered devices and a machine learning algorithm for detecting the position and orientation of such devices from imagery. Such predictions can subsequently be displayed to an operator to improve the speed and accuracy by which they perform procedures.

Detecting a vessel region in multiple angiographic image frames and defining a direction that perpendicularly intersects a longitudinal direction of the vessel region to improve co-registration between two imaging modalities. Motion of the vessel region is then detected based on the direction that intersects the longitudinal direction of the vessel region by evaluating positions of the vessel region in the multiple angiographic image frames. The method includes defining an area based on the detected motion and the detected vessel region, detecting a marker of an imaging catheter disposed in the vessel region within the area and performing co-registration based on the detected marker.

Machine logic (for example, software) for registering multiple medical images, each showing a common lesion, with each other. In performing this registration, registration points are chosen to be both: (i) outside of image portion that is potentially compromised by the lesion (in any of the multiple images); and (ii) as close to the lesion as possible. However, in at least one of the images the extent of the lesion is not knownso, in order to accommodate this uncertainty about the lesion boundaries, lesion predicting machine logic rules are used to predict the size, shape and/or location of the lesion. Machine learning is used to intermittently adjust and improve the lesion predicting machine logic rules.